Existing work on sign language translation - that is, translation from sign
language videos into sentences in a written language - has focused mainly on
(1) data collected in a controlled environment or (2) data in a specific
domain, which limits the applicability to real-world settings. In this paper,
we introduce OpenASL, a large-scale American Sign Language (ASL) - English
dataset collected from online video sites (e.g., YouTube). OpenASL contains 288
hours of ASL videos in multiple domains from over 200 signers and is the
largest publicly available ASL translation dataset to date. To tackle the
challenges of sign language translation in realistic settings and without
glosses, we propose a set of techniques including sign search as a pretext task
for pre-training and fusion of mouthing and handshape features. The proposed
techniques produce consistent and large improvements in translation quality,
over baseline models based on prior work. Our data and code are publicly
available at this https URL

本文介绍了 OpenASL 数据集，包含超过 200 名大规模美国手语（ASL）- 英语翻译中对手语搜索作为预训练工作的技术和融合口型和手势特征等技术，这些技术相比基线模型产生了一致的显著提高。